In our modern life, smartphones, tablets, and laptops are the primary computing devices, while phones are especially indispensable for many people [1]}. With the development of the processors and sensors of these devices, like cameras and microphones, it's evident that an unprecedented amount of data would be produced when so many people carry them anywhere. Apparently, if we are able to exploit such data effectively and efficiently, we could easily train and improve our models to acquire much better performance. For instance, with the photos from the smart phones' cameras, we could train a better computer vision model to classify different images or segment different objects. With the text data people enter into their devices, we could obtain a more robust language model to translate different languages. However, it's often not feasible to directly exploit these data together, because the data from mobile devices are usually private and significantly large in quantity. Due to these reasons, it is insecure and impossible to gather all such data together to store in a centralized location. Federated learning was proposed by researchers to address these issues [2]}.
